Random Effects Models and Multistage Estimation Procedures for Statistical Population Reconstruction of Small Game Populations

Gast, Christopher M.; Skalski, John R.; Isabelle, Jason L.; Clawson, Michael V.

doi:10.1371/journal.pone.0065244

Cited by 15 publications

(27 citation statements)

References 20 publications

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“…Program PopRecon 2.0 produces estimates of age‐ and year‐specific abundance values and associated standard errors using the Horowitz–Thompson approach of Gast et al (,). Estimates of abundance for the youngest age class may also be considered an estimate of net annual recruitment for the population.…”

Section: Resultsmentioning

confidence: 99%

“…From the model fitting, recruitment numbers and population abundance are estimated either explicitly as in the case of Gove et al () or through a 2‐phase estimation process as in Gast et al (). In Program PopRecon, we used the Horvitz–Thompson approach advocated by Gast et al (,) where annual abundance is estimated by escalating the harvest counts by the estimated harvest and reporting rates.…”

Section: Methodsmentioning

confidence: 99%

“…Statistical population reconstruction has emerged as an option to help fill the demographic data gap in game management. Statistical population reconstruction has the ability to convert age‐at‐harvest data, commonly collected by wildlife management agencies, into estimates of animal abundance, harvest probabilities, natural survival probabilities, and recruitment (Gove et al ; Gast et al ,). In so doing, status and trends of the game population can be quantified and processes of recruitment, natural survival, and harvest mortality examined.…”

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confidence: 99%

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Performing statistical population reconstruction using Program PopRecon 2.0

et al. 2017

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Statistical population reconstruction develops an integrated likelihood model that incorporates age‐at‐harvest composition, harvest totals, hunter effort, and auxiliary data on survival, harvest, or abundance to model demographics of harvested populations. Each statistical population reconstruction model is handcrafted to account for varying levels of available demographic data, hunting regulations, sampling of the harvest process, and biology of the harvested species. PopRecon 2.0 is a software program that facilitates the creation and analysis of these unique demographic models and capable of analyzing either full or pooled age‐at‐harvest data using either fixed or random effects on the annual survival and harvest processes. The program produces annual estimates of abundance, harvest probabilities, natural survival, and recruitment using a user‐friendly, interactive computing environment and AD Model Builder analytical software. Program PopRecon 2.0 is demonstrated using 23 years (1993–2015) of harvest data on greater sage‐grouse (Centrocercus urophasianus) from Oregon, USA. © 2017 The Wildlife Society.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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confidence: 99%

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Performing statistical population reconstruction using Program PopRecon 2.0

et al. 2017

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“…We modeled partial observability by substituting an estimate of male abundance ( N̂ ) for the true abundance ( N ) in harvest models, mimicking a situation where male abundance estimates are used to update target harvests over time in a state‐dependent manner. We simulated abundance estimates as normal random variables,

{\hat{N}}_{m, s, t} ~ Normal (N_{m, s, t}, σ_{{\overset{N}{true}}_{m, s, t}})

, with the mean centered on the true abundance of males ( m ) at the start of spring hunting ( s ) in year t (

N_{m, s, t}

) and a constant coefficient of variation (

σ_{{\overset{N}{true}}_{m, s, t}} goodbreakinfix= {CV}_{{\overset{N}{true}}_{m, s}} goodbreakinfix\times N_{m, s, t} italic;

Supplement 1, available online in Supporting Information) that was estimated empirically from abundance estimates generated by Gast et al (). To understand the effects of partial observability on performance of harvest policies, we also replicated all simulations without observation error, assuming

σ_{{\overset{N}{true}}_{m, s, t}} =

0 and therefore

{\hat{N}}_{m, s, t} goodbreakinfix= N_{m, s, t}

.…”

Section: Methodsmentioning

confidence: 99%

Simulated performance of multi‐year harvest regulation cycles for wild turkeys

2019

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Wild turkeys (Meleagris gallopavo) are a prolific species and valuable game animal throughout the United States. Stochastic simulations are commonly used to inform harvest management, and we used simulation to test performance of fall harvest management that included 1‐, 3‐, and 5‐year cycles of population assessment and updating of harvest targets, respectively. To assess robustness of our conclusions, we replicated analyses across 18 combinations of model parameters that included population productivity (3 levels), sex‐specific vulnerability to fall harvest (3 levels), and magnitude of spring harvest (2 levels). Performance of multi‐year cycles, measured using abundance of males and annual harvest, depended on the context of model parameters that interacted to determine responses of populations to harvest. One‐ and 3‐year cycles had similar performance so long as female harvests were less than or equal to male harvests. However, when harvest of females was greater than males, or when 5‐year regulation cycles were implemented, there was greater risk due to nonlinear population responses to increased harvest. For example, nonlinearity resulted in thresholds where declines to abundance and harvest could occur with small increases to harvest rates, and thus the sustainability of fall harvests was less robust for multi‐year cycles with time‐lagged assessment and decision making. Moreover, the harvest rate resulting in threshold responses depended on model parameters and often occurred within the range of harvest rates recommended by earlier modeling studies (7–15%). Our results imply that multi‐year cycles can be a viable approach to harvest management. Monitoring that provides information on sex‐specific harvest is recommended, however, to determine if nonlinear population responses should be anticipated. Ideally, information on population‐specific vital rates would also be available to allow managers to avoid harvest rates near thresholds that are expected to result in population declines. © The Wildlife Society, 2019

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“…We calculated standard errors from a numerical estimate of the inverse Hessian (Skalski et al 2012b, Gast et al 2013a,b) using the numDeriv package (Gilbert and Varadhan ). Because these reconstruction models consistently underestimate uncertainty (Gast ), we inflated all standard errors and confidence intervals by the goodness‐of‐fit scale parameter suggested by Skalski et al (, ):

\sqrt{\frac{χ_{df}^{2}}{df}},

where the

χ^{2}

statistic is based on the observed age‐at‐harvest data (

h_{i j}

) and their expected values under the fitted likelihood model.…”

Section: Methodsmentioning

confidence: 99%

Utility of radio‐telemetry data for improving statistical population reconstruction

et al. 2017

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Statistical population reconstruction using age‐at‐harvest and catch‐effort data has recently emerged as a robust and versatile approach to estimating the demographic dynamics of harvested populations of wildlife. Although there are clear benefits to incorporating radio‐telemetry data into reconstruction efforts, these data are costly and time‐consuming to collect. Managers that consider collecting these data alongside existing efforts could benefit from a comprehensive examination of how such benefits are influenced by the amount of radio‐telemetry data collected. Using a harvested population of American marten (Martes americana) in northeastern Minnesota, USA as a case study, we investigated the performance of population reconstruction using information on natural, harvest, or combined mortality derived from radio‐telemetry data collected over different numbers of years and with different numbers of animals collared each year. We simulated populations under a range of conditions and determined that incorporating radio‐telemetry data on natural and harvest mortality significantly improved model precision, and that each additional animal collared per year yielded a 0.50 ± 0.14% (SE) improvement in precision, whereas every additional year of radio‐telemetry data resulted in a 2.42 ± 0.70% improvement. Thus, including another year of radio‐telemetry resulted in similar gains in precision as including approximately 5 additional animals collared per year. In our applied marten example, incorporating radio‐telemetry data resulted in a significantly higher estimate of trapping vulnerability (0.20 vs. 0.058) and an overall smaller population size than reconstruction based solely on age‐at‐harvest and trapper effort data. These results illustrate the benefits of performing auxiliary studies, caution against relying on the results of population reconstruction based solely on age‐at‐harvest and hunter‐effort data, and demonstrate that improvements from incorporating radio‐telemetry data become evident even after as few as 2 years of data collection. © 2017 The Wildlife Society.

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Random Effects Models and Multistage Estimation Procedures for Statistical Population Reconstruction of Small Game Populations

Cited by 15 publications

References 20 publications

Performing statistical population reconstruction using Program PopRecon 2.0

Performing statistical population reconstruction using Program PopRecon 2.0

Simulated performance of multi‐year harvest regulation cycles for wild turkeys

Utility of radio‐telemetry data for improving statistical population reconstruction

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